A waterbody typology derived from catchment controls using self-organising maps

Multiple catchment controls contribute to the geomorphic functioning of river systems at the reach-level, yet only a limited number are usually considered by river scientists and managers. This study uses multiple morphometric, geological, climatic and anthropogenic catchment characteristics to produce a single national typology of catchment controls in England and Wales. Self-organising maps, a machine learning technique, are used to reduce the complexity of the GIS-derived characteristics to classify 4485 Water Framework Directive waterbodies into seven types. The waterbody typology is mapped across England and Wales, primarily reflecting an upland to lowland gradient in catchment controls and secondarily reflecting the heterogeneity of the catchment landscape. The seven waterbody types are evaluated using reach-level physical habitat indices (including measures of sediment size, flow, channel modification and diversity) extracted from River Habitat Survey data. Significant differences are found between each of the waterbody types for most habitat indices suggesting that the GIS-derived typology has functional application for reach-level habitats. This waterbody typology derived from catchment controls is a valuable tool for understanding catchment influences on physical habitats. It should prove useful for rapid assessment of catchment controls for river management, especially where regulatory compliance is based on reach-level monitoring

The “dirty dozen” of freshwater science: Detecting then reconciling hydrological data biases and errors

Sound water policy and management rests on sound hydrometeorological and ecological data. Conversely, unrepresentative, poorly collected or erroneously archived data introduces uncertainty regarding the magnitude, rate and direction of environmental change, in addition to undermining confidence in decision-making processes. Unfortunately, data biases and errors can enter the information flow at various stages, starting with site selection, instrumentation, sampling/ measurement procedures, post-processing and ending with archiving systems. Techniques such as visual inspection of raw data, graphical representation and comparison between sites, outlier and trend detection, and referral to metadata can all help uncover spurious data. Tell-tale signs of ambiguous and/or anomalous data are highlighted using 12 carefully chosen cases drawn mainly from hydrology (‘the dirty dozen’). These include evidence of changes in site or local conditions (due to land management, river regulation or urbanisation); modifications to instrumentation or inconsistent observer behaviour; mismatched or misrepresentative sampling in space and time; treatment of missing values, post-processing and data storage errors. As well as raising awareness of pitfalls, recommendations are provided for uncovering lapses in data quality after the information has been gathered. It is noted that error detection and attribution are more problematic for very large data sets, where observation networks are automated, or when various information sources have been combined. In these cases, more holistic indicators of data integrity are needed that reflect the overall information life-cycle and application(s) of the hydrological data

Integrating network topology metrics into studies of catchment-level effects on river characteristics

© 2019 Author(s). The spatial arrangement of the river network is a fundamental characteristic of the catchment, acting as a conduit between catchment-level effects and reach morphology and ecology. Yet river network structure is often simplified to reflect an upstream-to-downstream gradient of river characteristics, commonly represented by stream order. The aim of this study is to quantify network topological structure using two network density metrics-one that represents network density over distance and the other over elevation-that can easily be extracted from digital elevation models and so may be applied to any catchment across the globe. These metrics should better account for the multi-dimensional nature of the catchment than stream order and be functionally applicable across geomorphological, hydrological and ecological attributes of the catchment. The functional utility of the metrics is assessed by appropriating monitoring data collected for regulatory compliance to explore patterns of river characteristics in relation to network topology. This method is applied to four comparatively low-energy, anthropogenically modified catchments in the UK using river characteristics derived from England's River Habitat Survey database. The patterns in river characteristics explained by network density metrics are compared to stream order as a standard measure of topology. The results indicate that the network density metrics offer a richer and functionally more relevant description of network topology than stream order, highlighting differences in the density and spatial arrangement of each catchment's internal network structure. Correlations between the network density metrics and river characteristics show that habitat quality score consistently increases with network density in all catchments as hypothesized. For other measures of river character-modification score, flow-type speed and sediment size-there are varying responses in different catchments to the two network density metrics. There are few significant correlations between stream order and the river characteristics, highlighting the limitations of stream order in accounting for network topology. Overall, the results suggest that network density metrics are more powerful measures which conceptually and functionally provide an improved method of accounting for the impacts of network topology on the fluvial system

Integrating network topology metrics into studies of catchment-level effects on habitat diversity

The spatial arrangement of the river network is a fundamental characteristic of the catchment, acting as a conduit between catchment-level effects and in-channel morphology and ecology. Yet river network structure is often simplified to reflect an up-to-downstream gradient of in-channel features, commonly represented by stream order. The aim of this study is to quantify network topological structure using new metrics – distance network density and elevation network density – that better account for the multi-dimensional nature of the catchment and which are functionally applicable across geomorphological, hydrological and ecological attributes of the catchment. The functional utility of the metrics in explaining patterns of physical habitat diversity is assessed in comparison to stream order. The metrics are calculated for four low-energy, anthropogenically modified catchments in the UK and compared to a physical habitat diversity score derived from England’s River Habitat Survey. The results indicate that the new metrics offer a richer, and functionally more-relevant description of network topology than stream order, highlighting differences in the density and spatial arrangement of each catchment’s internal network structure. Correlations between the new metrics and physical habitat diversity score show that distance network density is positively related to maximum habitat diversity in three of the four catchments. There is also evidence that increased distance network density may reduce minimum habitat diversity in catchments with greater anthropogenic modification. When all catchments are combined, distance network density is positively correlated with maximum, mean and minimum habitat diversity. There are no significant correlations between elevation network density and habitat diversity. In all but the largest streams, there is no significant relation between habitat diversity and stream order highlighting the limitations of stream order in accounting for network topology. Overall, the results suggest that distance network density is a more powerful metric which conceptually provides an improved method of accounting for the impacts of network topology on the fluvial system exhibiting strong relationships with habitat diversity, particularly maximum habitat diversity